Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Antholzer, Stephan"'
Autoencoders allow to reconstruct a given input from a small set of parameters. However, the input size is often limited due to computational costs. We therefore propose a clustering and reassembling method for volumetric point clouds, in order to al
Externí odkaz:
http://arxiv.org/abs/2211.01009
Autor:
Antholzer, Stephan, Haltmeier, Markus
Deep learning based reconstruction methods deliver outstanding results for solving inverse problems and are therefore becoming increasingly important. A recently invented class of learning-based reconstruction methods is the so-called NETT (for Netwo
Externí odkaz:
http://arxiv.org/abs/2011.03627
Filtered backprojection (FBP) is an efficient and popular class of tomographic image reconstruction methods. In photoacoustic tomography, these algorithms are based on theoretically exact analytic inversion formulas which results in accurate reconstr
Externí odkaz:
http://arxiv.org/abs/1908.00593
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of liver lesions in CT images allows assessment of tumor load, treatment
Externí odkaz:
http://arxiv.org/abs/1902.07971
Autor:
Antholzer, Stephan, Schwab, Johannens, Bauer-Marschallinger, Johannes, Burgholzer, Peter, Haltmeier, Markus
We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab, S. Antholzer, and M. Haltmeier. NETT: Solving Inverse Problems with De
Externí odkaz:
http://arxiv.org/abs/1901.11158
We investigate compressed sensing (CS) techniques for reducing the number of measurements in photoacoustic tomography (PAT). High resolution imaging from CS data requires particular image reconstruction algorithms. The most established reconstruction
Externí odkaz:
http://arxiv.org/abs/1901.06510
Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms w
Externí odkaz:
http://arxiv.org/abs/1901.06506
We develop a data-driven regularization method for the severely ill-posed problem of photoacoustic image reconstruction from limited view data. Our approach is based on the regularizing networks that have been recently introduced and analyzed in [J.
Externí odkaz:
http://arxiv.org/abs/1901.06498
Deep learning and (deep) neural networks are emerging tools to address inverse problems and image reconstruction tasks. Despite outstanding performance, the mathematical analysis for solving inverse problems by neural networks is mostly missing. In t
Externí odkaz:
http://arxiv.org/abs/1812.00965
Recently, deep learning based methods appeared as a new paradigm for solving inverse problems. These methods empirically show excellent performance but lack of theoretical justification; in particular, no results on the regularization properties are
Externí odkaz:
http://arxiv.org/abs/1806.06137